from datetime import timedelta
from utils.operators.cluster_for_spark_sql_operator import SparkSqlOperator
from jms.dm.dm_tab_scan_piece_detail_dt import jms_dm__dm_tab_scan_piece_detail_dt
from jms.dm.dm_tab_scan_unpacking_detail_dt import jms_dm__dm_tab_scan_unpacking_detail_dt

jms_dm__dm_tab_scan_piece_summary_dt = SparkSqlOperator(
    task_id='jms_dm__dm_tab_scan_piece_summary_dt',
    task_concurrency=1,
    depends_on_past=True,
    pool_slots=2,
    master='yarn',
    execution_timeout=timedelta(minutes=30),
    email=['jokic.wang@jtexpress.com', 'yl_bigdata@yl-scm.com'],
    name='jms_dm__dm_tab_scan_piece_summary_dt_{{ execution_date | date_add(1) | cst_ds }}',
    sql='jms/dm/dm_tab_scan_piece_summary_dt/execute.hql',
    driver_memory='4G',
    driver_cores=2,
    executor_cores=4,
    executor_memory='6G',
    num_executors=15,
    conf={'spark.dynamicAllocation.enabled': 'true',  # 动态资源开启
          'spark.shuffle.service.enabled': 'true',  # 动态资源 Shuffle 服务开启
          'spark.dynamicAllocation.maxExecutors': 18,
          'spark.dynamicAllocation.cachedExecutorIdleTimeout': 120,  # 动态资源自动释放闲置 Executor 的超时时间(s)
          'spark.sql.sources.partitionOverwriteMode': 'dynamic',  # 允许删改已存在的分区
          'spark.sql.shuffle.partitions': 180,
          'spark.hadoop.hive.exec.dynamic.partition.mode': 'true',
          'spark.network.timeout': 900,
          'spark.executor.memoryOverhead': '2G',  # 堆外内存
          'spark.executor.extraJavaOptions': '-XX:+UseG1GC -XX:ParallelGCThreads=4'
          },
hiveconf={'hive.exec.dynamic.partition': 'true',  # 动态分区
              'hive.exec.dynamic.partition.mode': 'nonstrict',
              'hive.exec.max.dynamic.partitions': 200,  # 每天生成 20 个分区
              'hive.exec.max.dynamic.partitions.pernode': 200,  # 每天生成 20 个分区
              },
    yarn_queue='pro',
)

jms_dm__dm_tab_scan_piece_summary_dt << [jms_dm__dm_tab_scan_piece_detail_dt, jms_dm__dm_tab_scan_unpacking_detail_dt
                                         ]
